• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

闭环人工胰腺中“模糊逻辑”控制器的使用。

Use of a "fuzzy logic" controller in a closed-loop artificial pancreas.

机构信息

Department of Pediatrics, University of Washington, Seattle, WA, USA.

出版信息

Diabetes Technol Ther. 2013 Aug;15(8):628-33. doi: 10.1089/dia.2013.0036. Epub 2013 Jul 5.

DOI:10.1089/dia.2013.0036
PMID:23829285
Abstract

BACKGROUND

Most current model-based approaches to closed-loop artificial pancreas systems rely on mathematical equations describing the human glucoregulatory system; however, incorporating the various physiological parameters (e.g., illness, stress) into these models has been problematic. We evaluated a fully automated "fuzzy logic" (FL) closed-loop insulin dosing controller that does not require differential equations of the glucoregulatory system and allows clinicians to personalize dosing aggressiveness to meet individual patient requirements.

SUBJECTS AND METHODS

This pilot study evaluated the FL controller in the setting of bed rest in a very controlled environment. Two carbohydrate-controlled meals were given (30 g at 8 a.m. and 60 g at 2 p.m. without meal announcement or premeal bolus. The primary end point of the study was avoidance of hypoglycemia, defined at <60 mg/dL. Multiple end points related to the frequency and severity of hyperglycemia and hypoglycemia were also assessed.

RESULTS

Of the 12 subjects we recruited, 10 were enrolled, and seven completed the study. Two of the enrolled subjects were discontinued because of hypoglycemia; the other was discontinued because of sensor failure. Seven of the 10 subjects who completed the study had average blood glucose values of 165 mg/dL and were within a specified target blood glucose range (70-200 mg/dL) for 76% of the 24-h study period.

CONCLUSIONS

Our findings suggest that the FL controller provides a viable alternative to model-based controllers as a component of a closed-loop insulin delivery system. Furthermore, our FL controller allows clinicians to easily specify the level of glucose control based on each patient's clinical needs.

摘要

背景

大多数基于模型的闭环人工胰腺系统方法都依赖于描述人体血糖调节系统的数学方程;然而,将各种生理参数(例如疾病、压力)纳入这些模型一直存在问题。我们评估了一种完全自动化的“模糊逻辑”(FL)闭环胰岛素给药控制器,它不需要血糖调节系统的微分方程,并允许临床医生根据个体患者的需求个性化给药强度。

研究对象和方法

这项初步研究在非常受控的环境中评估了卧床休息期间的 FL 控制器。给予了两次碳水化合物控制的餐食(8 点 30 克,2 点 60 克,没有餐食通知或餐前 bolus。该研究的主要终点是避免低血糖,定义为<60mg/dL。还评估了与高血糖和低血糖的频率和严重程度相关的多个终点。

结果

在我们招募的 12 名受试者中,有 10 名入组,其中 7 名完成了研究。两名入组的受试者因低血糖而被停用;另一名因传感器故障而被停用。在完成研究的 10 名受试者中,有 7 名受试者的平均血糖值为 165mg/dL,并且在 24 小时研究期间的 76%时间内处于规定的目标血糖范围(70-200mg/dL)内。

结论

我们的发现表明,FL 控制器作为闭环胰岛素输送系统的组成部分,为基于模型的控制器提供了一种可行的替代方案。此外,我们的 FL 控制器允许临床医生根据每个患者的临床需求轻松指定血糖控制水平。

相似文献

1
Use of a "fuzzy logic" controller in a closed-loop artificial pancreas.闭环人工胰腺中“模糊逻辑”控制器的使用。
Diabetes Technol Ther. 2013 Aug;15(8):628-33. doi: 10.1089/dia.2013.0036. Epub 2013 Jul 5.
2
Closed-Loop Control Without Meal Announcement in Type 1 Diabetes.闭环控制无需告知用餐在 1 型糖尿病中的应用。
Diabetes Technol Ther. 2017 Sep;19(9):527-532. doi: 10.1089/dia.2017.0078. Epub 2017 Aug 2.
3
The "Glucositter" overnight automated closed loop system for type 1 diabetes: a randomized crossover trial.“Glucositter” 1 型糖尿病夜间自动化闭环系统:一项随机交叉试验。
Pediatr Diabetes. 2013 May;14(3):159-67. doi: 10.1111/pedi.12025. Epub 2013 Feb 28.
4
Stress Testing of an Artificial Pancreas System With Pizza and Exercise Leads to Improvements in the System's Fuzzy Logic Controller.用披萨和运动对人工胰腺系统进行压力测试可改进该系统的模糊逻辑控制器。
J Diabetes Sci Technol. 2015 Sep 14;9(6):1253-9. doi: 10.1177/1932296815602098.
5
Night glucose control with MD-Logic artificial pancreas in home setting: a single blind, randomized crossover trial-interim analysis.家庭环境中使用 MD-Logic 人工胰腺进行夜间血糖控制:一项单盲、随机交叉试验的中期分析。
Pediatr Diabetes. 2014 Mar;15(2):91-9. doi: 10.1111/pedi.12071. Epub 2013 Aug 15.
6
Multicenter closed-loop insulin delivery study points to challenges for keeping blood glucose in a safe range by a control algorithm in adults and adolescents with type 1 diabetes from various sites.多中心闭环胰岛素给药研究指出,对于来自不同地点的1型糖尿病成人和青少年,通过控制算法将血糖维持在安全范围内存在挑战。
Diabetes Technol Ther. 2014 Oct;16(10):613-22. doi: 10.1089/dia.2014.0066. Epub 2014 Jul 8.
7
Fully Closed-Loop Multiple Model Probabilistic Predictive Controller Artificial Pancreas Performance in Adolescents and Adults in a Supervised Hotel Setting.闭环多模型概率预测控制器人工胰腺在监督下的宾馆环境中对青少年和成年人的性能。
Diabetes Technol Ther. 2018 May;20(5):335-343. doi: 10.1089/dia.2017.0424. Epub 2018 Apr 16.
8
Sensitivity of the Predictive Hypoglycemia Minimizer System to the Algorithm Aggressiveness Factor.预测性低血糖最小化系统对算法激进因子的敏感性。
J Diabetes Sci Technol. 2015 Jun 30;10(1):104-10. doi: 10.1177/1932296815593292.
9
The challenges of achieving postprandial glucose control using closed-loop systems in patients with type 1 diabetes.使用闭环系统实现 1 型糖尿病患者餐后血糖控制的挑战。
Diabetes Obes Metab. 2018 Feb;20(2):245-256. doi: 10.1111/dom.13052. Epub 2017 Aug 10.
10
MD-logic artificial pancreas system: a pilot study in adults with type 1 diabetes.MD-logic 人工胰腺系统:1 型糖尿病成人患者的初步研究。
Diabetes Care. 2010 May;33(5):1072-6. doi: 10.2337/dc09-1830. Epub 2010 Feb 11.

引用本文的文献

1
Reinforcement Learning: A Paradigm Shift in Personalized Blood Glucose Management for Diabetes.强化学习:糖尿病个性化血糖管理的范式转变
Biomedicines. 2024 Sep 21;12(9):2143. doi: 10.3390/biomedicines12092143.
2
Exploring the progress of artificial intelligence in managing type 2 diabetes mellitus: a comprehensive review of present innovations and anticipated challenges ahead.探索人工智能在2型糖尿病管理中的进展:对当前创新及未来预期挑战的全面综述
Front Clin Diabetes Healthc. 2023 Dec 15;4:1316111. doi: 10.3389/fcdhc.2023.1316111. eCollection 2023.
3
An overview of advancements in closed-loop artificial pancreas system.
闭环人工胰腺系统进展概述
Heliyon. 2022 Nov 14;8(11):e11648. doi: 10.1016/j.heliyon.2022.e11648. eCollection 2022 Nov.
4
Control of blood glucose induced by meals for type-1 diabetics using an adaptive backstepping algorithm.利用自适应反推算法控制 1 型糖尿病患者餐后血糖
Sci Rep. 2022 Jul 18;12(1):12228. doi: 10.1038/s41598-022-16535-2.
5
Recent advances in closed-loop insulin delivery.闭环胰岛素输送的最新进展。
Metabolism. 2022 Feb;127:154953. doi: 10.1016/j.metabol.2021.154953. Epub 2021 Dec 7.
6
Embedded Model Predictive Control for a Wearable Artificial Pancreas.用于可穿戴人工胰腺的嵌入式模型预测控制
IEEE Trans Control Syst Technol. 2020 Nov;28(6):2600-2607. doi: 10.1109/tcst.2019.2939122. Epub 2019 Sep 18.
7
Intelligent automated drug administration and therapy: future of healthcare.智能自动化药物管理和治疗:医疗保健的未来。
Drug Deliv Transl Res. 2021 Oct;11(5):1878-1902. doi: 10.1007/s13346-020-00876-4. Epub 2021 Jan 14.
8
Chatteringfree hybrid adaptive neuro-fuzzy inference system-particle swarm optimisation data fusion-based BG-level control. chatterfree 混合自适应神经模糊推理系统-粒子群优化数据融合 BG 级控制。
IET Syst Biol. 2020 Feb;14(1):31-38. doi: 10.1049/iet-syb.2018.5019.
9
Do-It-Yourself Automated Insulin Delivery: A Leading Example of the Democratization of Medicine.自助式自动化胰岛素输送:医学民主化的典范。
J Diabetes Sci Technol. 2020 Sep;14(5):878-882. doi: 10.1177/1932296819890623. Epub 2019 Dec 26.
10
Plasma-Insulin-Cognizant Adaptive Model Predictive Control for Artificial Pancreas Systems.用于人工胰腺系统的血浆胰岛素感知自适应模型预测控制
J Process Control. 2019 May;77:97-113. doi: 10.1016/j.jprocont.2019.03.009. Epub 2019 Apr 10.